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6 results for “Choice probability”
Data from: A probable case of incipient speciation in Schizocosa wolf spiders driven by allochrony, habitat use and female mate choice
There is growing evidence that speciation can occur between populations that are not geographically isolated. The emergence of assortative mating is believed to be critical to this process, but how assortative mating arises in diverging populations is poorly understood. The wolf spider genus Schizocosa has become a model system for studying mechanisms of assortative mating. We conducted a series of experiments to identify the factors that control mate pair formation in a Schizocosa population that includes both ornamented and non-ornamented males. We show that the population also includes two previously unrecognised female phenotypes. One female phenotype mates mostly or exclusively with ornamented males, and the other mates mostly or exclusively with unornamented males. Assortative mating within these groups is maintained by differences in maturation time, microhabitat use, and female mate preference. We conclude that the population is not a single species as previously believed, but rather an incipient species pair with multiple overlapping mechanisms of reproductive isolation. The identification of a new incipient species pair in the well-studied and rapidly speciating Schizocosa clade presents new opportunities for the study of speciation without geographic isolation.
Data from: Ecological context and the probability of mistakes underlie speed choice
1.Movement is fundamental to the ecology of animals, and an animal's choice of movement speed determines the duration, energetic costs, and probability of success of any given activity. It is often assumed that animals should use maximum speeds when escaping from predators, but an increasing number of studies find animals rarely move as fast as they can in nature because faster speeds come with a greater chance of mistakes. Mathematical modelling suggests that, when escaping predators, prey animals should optimise speeds to simultaneously outrun their pursuer and minimise their probability of slipping. This can be particularly important when running along narrow structures like branches. When foraging, however, animals should avoid moving at high speeds, which are often energetically costly and decrease the ability to detect food or predators. 2.In this study, we examined how trade-offs between speed and probability of slipping influenced the speed choice of wild antechinus (Antechinus mysticus) during escaping and foraging behaviours. We also examined how this trade-off affected foraging behaviour. 3.Antechinus ran significantly faster when escaping (1.207 ± 0.033 ms-1) than foraging (0.145 ± 0.002 ms-1), and slipped 37% more often during escapes. However, foraging antechinus still slipped frequently on narrow branches, despite running an order of magnitude more slowly than they did on wide branches. Furthermore, antechinus slipped at lower speeds when foraging than they did when escaping, suggesting that avoiding mistakes is less highly prioritized when foraging. 4.Antechinus visited the feeding station accessed by a wide branch more frequently (and ate more while there) compared with feeding stations accessed by narrow branches, even when those branches were 33% or 67% shorter. This suggests that foraging decisions may be based on potential limitations to speed and the probability of slipping over distance to cover. 5.Though activities such as running can be fundamental to animals' fitness, a general framework to understand how animals select speeds in nature is still being developed. We test the assumption that animals choose running speeds to minimise their motor mistakes, and demonstrate the cost of mistakes is likely to be different across ecological and behavioural contexts.
Computed Tomography as the First-Choice Diagnostics in High Pre-Test Probability of Coronary Artery Disease
ClinicalTrials.gov study NCT02591992. IPD Sharing: Not stated. Countries: 1. Publications: 1.
Data from: Ecological context and the probability of mistakes underlie speed choice
Open the record for dataset details and reuse information.
Data from: A probable case of incipient speciation in Schizocosa wolf spiders driven by allochrony, habitat use and female mate choice
Open the record for dataset details and reuse information.
Parameters and predicted probabilities of mode choice and ride pass subscription for microtransit in Arlington, TX
<p>We provide parameters and predicted probabilities of mode choice (at trip level) and ride pass subscription (at individual level) for microtransit in Arlington, TX. The parameters are estimated by <a href="https://arxiv.org/abs/2408.12577">an agent-based, nested behavioral model</a> that is developed in the C2SMARTER project “Multi-modal Tripchain Planner for Disadvantaged Travelers to Incentivize Transit Usage” (Award #69A3551747124). We separate the choice related to microtransit into two parts: travel mode choice and ride pass subscription choice. Synthetic population data from <a href="https://www.replicahq.com/">Replica Inc.</a> and microtransit service data from <a href="https://www.arlingtontx.gov/">City of Arlington</a> are used for estimation. </p> <p>In the lower-branch travel mode choice, individuals decide on mode to use by considering factors such as travel time, cost, trip purpose, tour type, and mode-specific preferences. The mode choice set includes driving, walking, biking, carpool, and microtransit. Different from traditional mode choice models, we allow time and cost parameters to vary across individuals, and we allow mode specific constants to vary across trip OD pairs. This makes sense when we do not have sufficient data for socioeconomic attributes and built environment variables. The assumption we made here is that the impacts of these unobserved variables are included in the nonparametric distribution of individual and OD pair-level parameters.</p> <p>In the upper-branch ride pass subscription choice, individuals decide whether to purchase a weekly ride pass, a monthly ride pass, or no ride pass at all. By subscribing to a ride pass, travelers pay an amount of money in advance and enjoy free microtransit trips until the ride pass expires. The utility of purchasing a ride pass consists of four components: (1) the utility related to the prices of ride passes, (2) the utility related to the change in consumer surplus (or compensating variation) brought by free microtransit trips with a ride pass, (3) the utility specific to microtransit users, and (4) the alternative specific constant of the ride pass. Given the data availability, we consider the ride pass model as a simple MNL model with six parameters to calibrate. These ride pass parameters are calibrated using the Nelder-Mead Simplex Method. The cost function to minimize is the squared distance between the predicted ride pass market share and the observed one.</p> <p>Accordingly, this dataset consists of six .csv files:</p> <ul> <li>Mode_choice_parameters_weekday.csv</li> <li>Mode_choice_parameters_weekend.csv</li> <li>RidePass_subscription_parameters.csv</li> <li>Mode_choice_probability_weekday.csv</li> <li>Mode_choice_probability_weekend.csv</li> <li>Ridepass_subscription_probability.csv</li> </ul> <p> </p> <p><strong>Field definition in "Mode_choice_parameters_weekday.csv" and "Mode_choice_parameters_weekend.csv"</strong></p> <table> <tbody> <tr> <td><strong>Field Name</strong></td> <td><strong>Description</strong></td> </tr> <tr> <td>iid</td> <td>IDs of each synthetic individual</td> </tr> <tr> <td>trip_id</td> <td>IDs of each synthetic trip</td> </tr> <tr> <td>origin_bgrp</td> <td>FIPs code of the block group where the trip starts</td> </tr> <tr> <td>destination_bgrp</td> <td>FIPs code of the block group where the trip ends</td> </tr> <tr> <td>B_AUTO_TT</td> <td>The parameter of auto travel time (vary across individuals)</td> </tr> <tr> <td>B_MICRO_TT</td> <td>The parameter of microtransit waiting time (vary across individuals)</td> </tr> <tr> <td>B_NON_AUTO_TT</td> <td>The parameter of non-auto travel time (vary across individuals)</td> </tr> <tr> <td>B_COST</td> <td>The parameter of travel cost (vary across individuals)</td> </tr> <tr> <td>ASC_MIRCO</td> <td>The alternative specific constant of microtransit (vary across trip OD pairs)</td> </tr> <tr> <td>ASC_DRIVING</td> <td>The alternative specific constant of driving (vary across trip OD pairs)</td> </tr> <tr> <td>ASC_BIKING</td> <td>The alternative specific constant of biking (vary across trip OD pairs)</td> </tr> <tr> <td>ASC_WALKING</td> <td>The alternative specific constant of walking (vary across trip OD pairs)</td> </tr> <tr> <td>MICRO_P_SHOPPING</td> <td>The interaction effect between microtransit and shopping trip purpose (generic)</td> </tr> <tr> <td>MICRO_P_SCHOOL</td> <td>The interaction effect between microtransit and school trip purpose (generic)</td> </tr> <tr> <td>MICRO_P_OTHER</td> <td>The interaction effect between microtransit and other trip purpose (generic)</td> </tr> <tr> <td>MICRO_T_COMMUTE</td> <td>The interaction effect between microtransit and commute tour type (generic)</td> </tr> <tr> <td>MICRO_T_HOME_BASED</td> <td>The interaction effect between microtransit and home-based tour type (generic)</td> </tr> </tbody> </table> <p> </p> <p><strong>Field definition in "RidePass_subscription_parameters.csv"</strong></p> <table> <tbody> <tr> <td><strong>Field Name</strong></td> <td><strong>Description</strong></td> </tr> <tr> <td>B_COST_RP</td> <td>A transfer factor from trip fare to ride pass price</td> </tr> <tr> <td>B_CS_WEEKDAY</td> <td>The parameter of increased consumer surplus (due to the ride pass) on weekday</td> </tr> <tr> <td>B_CS_WEEKEND</td> <td>The parameter of increased consumer surplus (due to the ride pass) on weekend</td> </tr> <tr> <td>B_MICRO_USER</td> <td>The parameter of a binary variable indicating former microtransit users</td> </tr> <tr> <td>ASC_WRP</td> <td>The alternative specific constant of subscribing weekly ride pass</td> </tr> <tr> <td>ASC_MRP</td> <td>The alternative specific constant of subscribing monthly ride pass</td> </tr> </tbody> </table> <p> </p> <p><strong>Field definition in "Mode_choice_probability_weekday.csv" and "Mode_choice_probability_weekend.csv"</strong></p> <table> <tbody> <tr> <td><strong>Field Name</strong></td> <td><strong>Description</strong></td> </tr> <tr> <td>iid</td> <td>IDs of each synthetic individual</td> </tr> <tr> <td>trip_id</td> <td>IDs of each synthetic trip</td> </tr> <tr> <td>origin_bgrp</td> <td>FIPs code of the block group where the trip starts</td> </tr> <tr> <td>destination_bgrp</td> <td>FIPs code of the block group where the trip ends</td> </tr> <tr> <td>P_biking</td> <td>The predicted probability of choosing biking</td> </tr> <tr> <td>P_carpool</td> <td>The predicted probability of choosing carpool</td> </tr> <tr> <td>P_microtransit</td> <td>The predicted probability of choosing microtransit</td> </tr> <tr> <td>P_driving</td> <td>The predicted probability of choosing driving</td> </tr> <tr> <td>P_walking</td> <td>The predicted probability of choosing walking</td> </tr> </tbody> </table> <p> </p> <p><strong>Field definition in "Ridepass_subscription_probability.csv"</strong></p> <table> <tbody> <tr> <td><strong>Field Name</strong></td> <td><strong>Description</strong></td> </tr> <tr> <td>iid</td> <td>IDs of each synthetic individual</td> </tr> <tr> <td>Micro_user</td> <td>A binary variable indicating whether the individual used microtransit before</td> </tr> <tr> <td>Population_seg</td> <td>Segment ID of the synthetic individual</td> </tr> <tr> <td>BLOCKGROUP</td> <td>FIPs code of the home block group</td> </tr> <tr> <td>P_weekly_pass</td> <td>The predicted probability of subscribing weekly ride pass</td> </tr> <tr> <td>P_monthly_pass</td> <td>The predicted probability of subscribing monthly ride pass</td> </tr> <tr> <td>P_None</td> <td>The predicted probability of subscribing no ride pass</td> </tr> </tbody> </table>
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